Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory144.0 B

Variable types

Numeric8
Text1
Categorical9

Alerts

RowNumber is uniformly distributedUniform
RowNumber has unique valuesUnique
CustomerId has unique valuesUnique
Tenure has 413 (4.1%) zerosZeros
Balance has 3617 (36.2%) zerosZeros

Reproduction

Analysis started2024-05-26 20:20:59.069211
Analysis finished2024-05-26 20:21:31.908913
Duration32.84 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

RowNumber
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.5
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-26T21:21:32.661145image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.95
Q12500.75
median5000.5
Q37500.25
95-th percentile9500.05
Maximum10000
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.8957
Coefficient of variation (CV)0.5773214
Kurtosis-1.2
Mean5000.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum50005000
Variance8334166.7
MonotonicityStrictly increasing
2024-05-26T21:21:33.175823image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
6671 1
 
< 0.1%
6664 1
 
< 0.1%
6665 1
 
< 0.1%
6666 1
 
< 0.1%
6667 1
 
< 0.1%
6668 1
 
< 0.1%
6669 1
 
< 0.1%
6670 1
 
< 0.1%
6672 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
9999 1
< 0.1%
9998 1
< 0.1%
9997 1
< 0.1%
9996 1
< 0.1%
9995 1
< 0.1%
9994 1
< 0.1%
9993 1
< 0.1%
9992 1
< 0.1%
9991 1
< 0.1%

CustomerId
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15690941
Minimum15565701
Maximum15815690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-26T21:21:33.665567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum15565701
5-th percentile15578824
Q115628528
median15690738
Q315753234
95-th percentile15803034
Maximum15815690
Range249989
Interquartile range (IQR)124705.5

Descriptive statistics

Standard deviation71936.186
Coefficient of variation (CV)0.0045845681
Kurtosis-1.1961125
Mean15690941
Median Absolute Deviation (MAD)62432.5
Skewness0.0011491459
Sum1.5690941 × 1011
Variance5.1748149 × 109
MonotonicityNot monotonic
2024-05-26T21:21:34.143607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15634602 1
 
< 0.1%
15667932 1
 
< 0.1%
15766185 1
 
< 0.1%
15667632 1
 
< 0.1%
15599024 1
 
< 0.1%
15798709 1
 
< 0.1%
15741921 1
 
< 0.1%
15793671 1
 
< 0.1%
15797900 1
 
< 0.1%
15795933 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
15565701 1
< 0.1%
15565706 1
< 0.1%
15565714 1
< 0.1%
15565779 1
< 0.1%
15565796 1
< 0.1%
15565806 1
< 0.1%
15565878 1
< 0.1%
15565879 1
< 0.1%
15565891 1
< 0.1%
15565996 1
< 0.1%
ValueCountFrequency (%)
15815690 1
< 0.1%
15815660 1
< 0.1%
15815656 1
< 0.1%
15815645 1
< 0.1%
15815628 1
< 0.1%
15815626 1
< 0.1%
15815615 1
< 0.1%
15815560 1
< 0.1%
15815552 1
< 0.1%
15815534 1
< 0.1%
Distinct2932
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-05-26T21:21:35.229708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length23
Median length16
Mean length6.4349
Min length2

Characters and Unicode

Total characters64349
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1558 ?
Unique (%)15.6%

Sample

1st rowHargrave
2nd rowHill
3rd rowOnio
4th rowBoni
5th rowMitchell
ValueCountFrequency (%)
lo 33
 
0.3%
smith 32
 
0.3%
martin 29
 
0.3%
scott 29
 
0.3%
walker 28
 
0.3%
brown 26
 
0.3%
yeh 25
 
0.2%
shih 25
 
0.2%
genovese 25
 
0.2%
maclean 24
 
0.2%
Other values (2931) 9779
97.3%
2024-05-26T21:21:36.864663image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5799
 
9.0%
e 5764
 
9.0%
n 5235
 
8.1%
o 4905
 
7.6%
i 4491
 
7.0%
r 3547
 
5.5%
l 2921
 
4.5%
s 2592
 
4.0%
u 2552
 
4.0%
h 2150
 
3.3%
Other values (45) 24393
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53647
83.4%
Uppercase Letter 10299
 
16.0%
Other Punctuation 329
 
0.5%
Space Separator 55
 
0.1%
Dash Punctuation 19
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5799
10.8%
e 5764
10.7%
n 5235
 
9.8%
o 4905
 
9.1%
i 4491
 
8.4%
r 3547
 
6.6%
l 2921
 
5.4%
s 2592
 
4.8%
u 2552
 
4.8%
h 2150
 
4.0%
Other values (16) 13691
25.5%
Uppercase Letter
ValueCountFrequency (%)
C 1106
 
10.7%
M 1004
 
9.7%
B 707
 
6.9%
S 685
 
6.7%
H 661
 
6.4%
T 573
 
5.6%
L 545
 
5.3%
W 481
 
4.7%
P 466
 
4.5%
G 442
 
4.3%
Other values (15) 3629
35.2%
Other Punctuation
ValueCountFrequency (%)
' 237
72.0%
? 92
 
28.0%
Space Separator
ValueCountFrequency (%)
55
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63946
99.4%
Common 403
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5799
 
9.1%
e 5764
 
9.0%
n 5235
 
8.2%
o 4905
 
7.7%
i 4491
 
7.0%
r 3547
 
5.5%
l 2921
 
4.6%
s 2592
 
4.1%
u 2552
 
4.0%
h 2150
 
3.4%
Other values (41) 23990
37.5%
Common
ValueCountFrequency (%)
' 237
58.8%
? 92
 
22.8%
55
 
13.6%
- 19
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5799
 
9.0%
e 5764
 
9.0%
n 5235
 
8.1%
o 4905
 
7.6%
i 4491
 
7.0%
r 3547
 
5.5%
l 2921
 
4.5%
s 2592
 
4.0%
u 2552
 
4.0%
h 2150
 
3.3%
Other values (45) 24393
37.9%

CreditScore
Real number (ℝ)

Distinct460
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.5288
Minimum350
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-26T21:21:37.371031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile489
Q1584
median652
Q3718
95-th percentile812
Maximum850
Range500
Interquartile range (IQR)134

Descriptive statistics

Standard deviation96.653299
Coefficient of variation (CV)0.14857651
Kurtosis-0.42572568
Mean650.5288
Median Absolute Deviation (MAD)67
Skewness-0.071606608
Sum6505288
Variance9341.8602
MonotonicityNot monotonic
2024-05-26T21:21:37.900794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 233
 
2.3%
678 63
 
0.6%
655 54
 
0.5%
705 53
 
0.5%
667 53
 
0.5%
684 52
 
0.5%
670 50
 
0.5%
651 50
 
0.5%
683 48
 
0.5%
652 48
 
0.5%
Other values (450) 9296
93.0%
ValueCountFrequency (%)
350 5
0.1%
351 1
 
< 0.1%
358 1
 
< 0.1%
359 1
 
< 0.1%
363 1
 
< 0.1%
365 1
 
< 0.1%
367 1
 
< 0.1%
373 1
 
< 0.1%
376 2
 
< 0.1%
382 1
 
< 0.1%
ValueCountFrequency (%)
850 233
2.3%
849 8
 
0.1%
848 5
 
0.1%
847 6
 
0.1%
846 5
 
0.1%
845 6
 
0.1%
844 7
 
0.1%
843 2
 
< 0.1%
842 7
 
0.1%
841 12
 
0.1%

Geography
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
France
5014 
Germany
2509 
Spain
2477 

Length

Max length7
Median length6
Mean length6.0032
Min length5

Characters and Unicode

Total characters60032
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowSpain
3rd rowFrance
4th rowFrance
5th rowSpain

Common Values

ValueCountFrequency (%)
France 5014
50.1%
Germany 2509
25.1%
Spain 2477
24.8%

Length

2024-05-26T21:21:38.430436image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T21:21:38.869583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
france 5014
50.1%
germany 2509
25.1%
spain 2477
24.8%

Most occurring characters

ValueCountFrequency (%)
a 10000
16.7%
n 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50032
83.3%
Uppercase Letter 10000
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10000
20.0%
n 10000
20.0%
r 7523
15.0%
e 7523
15.0%
c 5014
10.0%
m 2509
 
5.0%
y 2509
 
5.0%
p 2477
 
5.0%
i 2477
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
F 5014
50.1%
G 2509
25.1%
S 2477
24.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 60032
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10000
16.7%
n 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10000
16.7%
n 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Male
5457 
Female
4543 

Length

Max length6
Median length4
Mean length4.9086
Min length4

Characters and Unicode

Total characters49086
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 5457
54.6%
Female 4543
45.4%

Length

2024-05-26T21:21:39.362070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T21:21:39.729452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
male 5457
54.6%
female 4543
45.4%

Most occurring characters

ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39086
79.6%
Uppercase Letter 10000
 
20.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14543
37.2%
a 10000
25.6%
l 10000
25.6%
m 4543
 
11.6%
Uppercase Letter
ValueCountFrequency (%)
M 5457
54.6%
F 4543
45.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 49086
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

Age
Real number (ℝ)

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.9218
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-26T21:21:40.254837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q132
median37
Q344
95-th percentile60
Maximum92
Range74
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.487806
Coefficient of variation (CV)0.26945841
Kurtosis1.3953471
Mean38.9218
Median Absolute Deviation (MAD)6
Skewness1.0113203
Sum389218
Variance109.99408
MonotonicityNot monotonic
2024-05-26T21:21:40.768923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 478
 
4.8%
38 477
 
4.8%
35 474
 
4.7%
36 456
 
4.6%
34 447
 
4.5%
33 442
 
4.4%
40 432
 
4.3%
39 423
 
4.2%
32 418
 
4.2%
31 404
 
4.0%
Other values (60) 5549
55.5%
ValueCountFrequency (%)
18 22
 
0.2%
19 27
 
0.3%
20 40
 
0.4%
21 53
 
0.5%
22 84
0.8%
23 99
1.0%
24 132
1.3%
25 154
1.5%
26 200
2.0%
27 209
2.1%
ValueCountFrequency (%)
92 2
 
< 0.1%
88 1
 
< 0.1%
85 1
 
< 0.1%
84 2
 
< 0.1%
83 1
 
< 0.1%
82 1
 
< 0.1%
81 4
< 0.1%
80 3
< 0.1%
79 4
< 0.1%
78 5
0.1%

Tenure
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0128
Minimum0
Maximum10
Zeros413
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-26T21:21:41.162911image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8921744
Coefficient of variation (CV)0.57695786
Kurtosis-1.1652252
Mean5.0128
Median Absolute Deviation (MAD)2
Skewness0.010991458
Sum50128
Variance8.3646726
MonotonicityNot monotonic
2024-05-26T21:21:41.712870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 1048
10.5%
1 1035
10.3%
7 1028
10.3%
8 1025
10.2%
5 1012
10.1%
3 1009
10.1%
4 989
9.9%
9 984
9.8%
6 967
9.7%
10 490
4.9%
ValueCountFrequency (%)
0 413
 
4.1%
1 1035
10.3%
2 1048
10.5%
3 1009
10.1%
4 989
9.9%
5 1012
10.1%
6 967
9.7%
7 1028
10.3%
8 1025
10.2%
9 984
9.8%
ValueCountFrequency (%)
10 490
4.9%
9 984
9.8%
8 1025
10.2%
7 1028
10.3%
6 967
9.7%
5 1012
10.1%
4 989
9.9%
3 1009
10.1%
2 1048
10.5%
1 1035
10.3%

Balance
Real number (ℝ)

ZEROS 

Distinct6382
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76485.889
Minimum0
Maximum250898.09
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-26T21:21:42.247722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median97198.54
Q3127644.24
95-th percentile162711.67
Maximum250898.09
Range250898.09
Interquartile range (IQR)127644.24

Descriptive statistics

Standard deviation62397.405
Coefficient of variation (CV)0.81580283
Kurtosis-1.4894118
Mean76485.889
Median Absolute Deviation (MAD)46766.79
Skewness-0.14110871
Sum7.6485889 × 108
Variance3.8934362 × 109
MonotonicityNot monotonic
2024-05-26T21:21:42.851353image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3617
36.2%
130170.82 2
 
< 0.1%
105473.74 2
 
< 0.1%
85304.27 1
 
< 0.1%
159397.75 1
 
< 0.1%
144238.7 1
 
< 0.1%
112262.84 1
 
< 0.1%
109106.8 1
 
< 0.1%
142147.32 1
 
< 0.1%
109109.33 1
 
< 0.1%
Other values (6372) 6372
63.7%
ValueCountFrequency (%)
0 3617
36.2%
3768.69 1
 
< 0.1%
12459.19 1
 
< 0.1%
14262.8 1
 
< 0.1%
16893.59 1
 
< 0.1%
23503.31 1
 
< 0.1%
24043.45 1
 
< 0.1%
27288.43 1
 
< 0.1%
27517.15 1
 
< 0.1%
27755.97 1
 
< 0.1%
ValueCountFrequency (%)
250898.09 1
< 0.1%
238387.56 1
< 0.1%
222267.63 1
< 0.1%
221532.8 1
< 0.1%
216109.88 1
< 0.1%
214346.96 1
< 0.1%
213146.2 1
< 0.1%
212778.2 1
< 0.1%
212696.32 1
< 0.1%
212692.97 1
< 0.1%

NumOfProducts
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
5084 
2
4590 
3
 
266
4
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Length

2024-05-26T21:21:43.423125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T21:21:43.932711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

HasCrCard
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
7055 
0
2945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Length

2024-05-26T21:21:44.446017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T21:21:44.877694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring characters

ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

IsActiveMember
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
5151 
0
4849 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Length

2024-05-26T21:21:45.312773image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T21:21:45.840942image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring characters

ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

EstimatedSalary
Real number (ℝ)

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100090.24
Minimum11.58
Maximum199992.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-26T21:21:46.397743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum11.58
5-th percentile9851.8185
Q151002.11
median100193.91
Q3149388.25
95-th percentile190155.38
Maximum199992.48
Range199980.9
Interquartile range (IQR)98386.137

Descriptive statistics

Standard deviation57510.493
Coefficient of variation (CV)0.57458642
Kurtosis-1.1815184
Mean100090.24
Median Absolute Deviation (MAD)49198.15
Skewness0.0020853577
Sum1.0009024 × 109
Variance3.3074568 × 109
MonotonicityNot monotonic
2024-05-26T21:21:47.477776image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24924.92 2
 
< 0.1%
101348.88 1
 
< 0.1%
55313.44 1
 
< 0.1%
72500.68 1
 
< 0.1%
182692.8 1
 
< 0.1%
4993.94 1
 
< 0.1%
124964.82 1
 
< 0.1%
161971.42 1
 
< 0.1%
39488.04 1
 
< 0.1%
187811.71 1
 
< 0.1%
Other values (9989) 9989
99.9%
ValueCountFrequency (%)
11.58 1
< 0.1%
90.07 1
< 0.1%
91.75 1
< 0.1%
96.27 1
< 0.1%
106.67 1
< 0.1%
123.07 1
< 0.1%
142.81 1
< 0.1%
143.34 1
< 0.1%
178.19 1
< 0.1%
216.27 1
< 0.1%
ValueCountFrequency (%)
199992.48 1
< 0.1%
199970.74 1
< 0.1%
199953.33 1
< 0.1%
199929.17 1
< 0.1%
199909.32 1
< 0.1%
199862.75 1
< 0.1%
199857.47 1
< 0.1%
199841.32 1
< 0.1%
199808.1 1
< 0.1%
199805.63 1
< 0.1%

Exited
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
7962 
1
2038 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Length

2024-05-26T21:21:47.972663image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T21:21:48.347627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Complain
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
7956 
1
2044 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Length

2024-05-26T21:21:48.821658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T21:21:49.203147image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
3
2042 
2
2014 
4
2008 
5
2004 
1
1932 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row5
5th row5

Common Values

ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Length

2024-05-26T21:21:49.691769image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T21:21:50.172925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Most occurring characters

ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Card Type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
DIAMOND
2507 
GOLD
2502 
SILVER
2496 
PLATINUM
2495 

Length

Max length8
Median length7
Mean length6.2493
Min length4

Characters and Unicode

Total characters62493
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDIAMOND
2nd rowDIAMOND
3rd rowDIAMOND
4th rowGOLD
5th rowGOLD

Common Values

ValueCountFrequency (%)
DIAMOND 2507
25.1%
GOLD 2502
25.0%
SILVER 2496
25.0%
PLATINUM 2495
24.9%

Length

2024-05-26T21:21:50.767842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T21:21:51.207718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
diamond 2507
25.1%
gold 2502
25.0%
silver 2496
25.0%
platinum 2495
24.9%

Most occurring characters

ValueCountFrequency (%)
D 7516
12.0%
I 7498
12.0%
L 7493
12.0%
O 5009
8.0%
A 5002
8.0%
M 5002
8.0%
N 5002
8.0%
G 2502
 
4.0%
S 2496
 
4.0%
V 2496
 
4.0%
Other values (5) 12477
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 62493
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 7516
12.0%
I 7498
12.0%
L 7493
12.0%
O 5009
8.0%
A 5002
8.0%
M 5002
8.0%
N 5002
8.0%
G 2502
 
4.0%
S 2496
 
4.0%
V 2496
 
4.0%
Other values (5) 12477
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62493
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 7516
12.0%
I 7498
12.0%
L 7493
12.0%
O 5009
8.0%
A 5002
8.0%
M 5002
8.0%
N 5002
8.0%
G 2502
 
4.0%
S 2496
 
4.0%
V 2496
 
4.0%
Other values (5) 12477
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 7516
12.0%
I 7498
12.0%
L 7493
12.0%
O 5009
8.0%
A 5002
8.0%
M 5002
8.0%
N 5002
8.0%
G 2502
 
4.0%
S 2496
 
4.0%
V 2496
 
4.0%
Other values (5) 12477
20.0%

Point Earned
Real number (ℝ)

Distinct785
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean606.5151
Minimum119
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-26T21:21:51.762731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile255
Q1410
median605
Q3801
95-th percentile960
Maximum1000
Range881
Interquartile range (IQR)391

Descriptive statistics

Standard deviation225.92484
Coefficient of variation (CV)0.37249664
Kurtosis-1.193781
Mean606.5151
Median Absolute Deviation (MAD)195
Skewness0.008344113
Sum6065151
Variance51042.033
MonotonicityNot monotonic
2024-05-26T21:21:52.328223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
408 26
 
0.3%
709 25
 
0.2%
244 23
 
0.2%
629 23
 
0.2%
503 22
 
0.2%
343 22
 
0.2%
564 22
 
0.2%
351 22
 
0.2%
240 22
 
0.2%
720 21
 
0.2%
Other values (775) 9772
97.7%
ValueCountFrequency (%)
119 1
 
< 0.1%
163 1
 
< 0.1%
206 1
 
< 0.1%
219 16
0.2%
220 7
0.1%
221 14
0.1%
222 11
0.1%
223 12
0.1%
224 9
0.1%
225 14
0.1%
ValueCountFrequency (%)
1000 13
0.1%
999 7
 
0.1%
998 12
0.1%
997 15
0.1%
996 2
 
< 0.1%
995 19
0.2%
994 17
0.2%
993 12
0.1%
992 13
0.1%
991 11
0.1%

Interactions

2024-05-26T21:21:27.199748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:03.467754image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:06.940397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:10.337814image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:13.671471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:16.957841image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:20.224372image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:23.814356image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:27.686380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:03.970521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:07.375103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:10.764918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:14.077844image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:17.357700image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:20.637858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:24.255263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:28.030579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:04.430382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:07.788954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:11.151027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:14.489016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:17.742583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:21.050938image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:24.717952image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:28.439098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:04.861297image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:08.172971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:11.610999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:14.882863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:18.161609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:21.453336image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:25.102895image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:28.833223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:05.304665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:08.678150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:12.006157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:15.306023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:18.560920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:22.324673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:25.576164image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:29.232797image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:05.710030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:09.088011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:12.418280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:15.727968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:18.967961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:22.624182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:25.952771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:29.691558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:06.098912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:09.525326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:12.847993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:16.127622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:19.371044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:23.023065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:26.417968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:30.100356image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:06.511461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:09.937911image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:13.290766image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:16.579206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:19.807944image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:23.433573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-26T21:21:26.809619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-05-26T21:21:30.739102image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-26T21:21:31.216190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RowNumberCustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedComplainSatisfaction ScoreCard TypePoint Earned
0115634602Hargrave619FranceFemale4220.00111101348.88112DIAMOND464
1215647311Hill608SpainFemale41183807.86101112542.58013DIAMOND456
2315619304Onio502FranceFemale428159660.80310113931.57113DIAMOND377
3415701354Boni699FranceFemale3910.0020093826.63005GOLD350
4515737888Mitchell850SpainFemale432125510.8211179084.10005GOLD425
5615574012Chu645SpainMale448113755.78210149756.71115DIAMOND484
6715592531Bartlett822FranceMale5070.0021110062.80002SILVER206
7815656148Obinna376GermanyFemale294115046.74410119346.88112DIAMOND282
8915792365He501FranceMale444142051.0720174940.50003GOLD251
91015592389H?684FranceMale272134603.8811171725.73003GOLD342
RowNumberCustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedComplainSatisfaction ScoreCard TypePoint Earned
9990999115798964Nkemakonam714GermanyMale33335016.6011053667.08003GOLD791
9991999215769959Ajuluchukwu597FranceFemale53488381.2111069384.71113GOLD369
9992999315657105Chukwualuka726SpainMale3620.00110195192.40005SILVER560
9993999415569266Rahman644FranceMale287155060.4111029179.52005DIAMOND715
9994999515719294Wood800FranceFemale2920.00200167773.55004PLATINUM311
9995999615606229Obijiaku771FranceMale3950.0021096270.64001DIAMOND300
9996999715569892Johnstone516FranceMale351057369.61111101699.77005PLATINUM771
9997999815584532Liu709FranceFemale3670.0010142085.58113SILVER564
9998999915682355Sabbatini772GermanyMale42375075.3121092888.52112GOLD339
99991000015628319Walker792FranceFemale284130142.7911038190.78003DIAMOND911